{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Describe the purpose of the data set you selected \n",
    "- *Why was this data collected in the first place?* - The data set selected is the \"UNSW_NB15\" network traffic data set. The data set was created to evalutate Network Intrusion Detection Systems (NIDS). According to the creators of the data set, N. Moustafa and J. Slay from the Australian Defence Force Academy, the \"quality of a NIDS data set reflects two important characteristics: comprehensive reflection of contemporary threats and an inclusive normal range of traffic [1].\" Before this data set was generated, they argued that existing data sets used to train and test NIDS were not representative of current traffic flows and threats.\n",
    "- Describe how you would define and measure the outcomes from the dataset. That is: \n",
    "    - *Why is this data important?* - The importance of this data set is that it will allow NIDS to be evaluted better, which will increase their performance, increase their protective power and reduce the chance of false positives and false negatives.\n",
    "    - *How do you know if you have mined useful knowledge from the dataset?* - We will know we mined useful knowledge from the data set if we can determine, based on a collection of packets of traffic data, whether the features of those packets indicate an attack or just normal traffic.\n",
    "    - *How would you measure the effectiveness of a good prediction algorithm? Be specific.* - Finding a model that predicts from the network traffic whether an attack is occuring or not, and, if there is an attack, which category of attack if occuring, will measure the effectiveness of the model. Determining if an attack is occuring is the basis of the model and with refinement, we will try to search for categorizing the attack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "from pandas.tools.plotting import scatter_matrix\n",
    "import pandas as pd\n",
    "import numpy  as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.simplefilter('ignore', DeprecationWarning)\n",
    "pd.set_option('display.max_columns', None)    # set the max columns to show to unlimited\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load UNSW_NB15 into a Pandas dataframe\n",
    "df = pd.read_csv('UNSW_NB15_training_set.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe the meaning and type of data (scale, values, etc.) for each attribute in the data file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UNSW_NB15_training_set\n",
    "- A Network Intrusion Data Set.\n",
    "    - 82332 rows x 45 columns\n",
    "        - **id** *int* The id of the record\n",
    "        - **dur** *float* Record's total duration\n",
    "        - **proto** *Nominal* Tranaction Protocol\n",
    "        - **service** *Nominal* http, ftp, ssh, dns, ..., else(-)\n",
    "        - **state** *Nominal* The state and its dependent protocol e.g. ACC, CLO, FIN, INT, ..., else (-)\n",
    "\t\t- **spkts** *int* Source to destination packet count\n",
    "\t\t- **dpkts** *int* Destination to source packet count \n",
    "\t\t- **sbytes** *int* Source to destination bytes\n",
    "\t\t- **dbytes** *int* Destination to source bytes\n",
    "\t\t- **rate** *float* We were unable to find a description of rate. It does contain outliers, but we were unable to find reason to throw it out. So we kept it at any *rate*\n",
    "\t\t- **sttl** *int* Source to destination bytes\n",
    "\t\t- **dttl** *int* Destination to source time to live\n",
    "\t\t- **sload** *float* Source bits per second\n",
    "\t\t- **dload** *float* Destination bits per second\n",
    "\t\t- **sloss** *int* Source packets retransmitted or dropped\n",
    "\t\t- **dloss** *int* Destination packets retransmitted or dropped\n",
    "\t\t- **sinpkt** *float* Source inter-packet arrival time(mSec)\n",
    "\t\t- **dinpkt** *float* Destination inetr-packet arrival time(mSec)\n",
    "\t\t- **sjit** *float* Source jitter (mSec)\n",
    "\t\t- **djit** *float* Destination jitter(mSec)\n",
    "\t\t- **swin** *int* Source TCP window advertisment\n",
    "\t\t- **stcpb** *int* Source TCP sequence number\n",
    "\t\t- **dtcpb** *int* Destination TCP sequence number\n",
    "\t\t- **dwin** *int* Destination TCP window advertisment\n",
    "\t\t- **tcprtt** *float* The sum of 'synack' and 'ackdat' of the TCP\n",
    "\t\t- **synack** *float* The time between the SYN and the SYN_ACK packets of the TCP\n",
    "\t\t- **ackdat** *float* The time between the SYN_ACK and the ACK packets of the TCP\n",
    "\t\t- **smean** *int* Mean of the flow packet size transmitted by the sre\n",
    "\t\t- **dmean** *int* Mean of the flow packet size transmitted by the dst\n",
    "\t\t- **trans_depth** *int* The depth into the connection of the http request/response transaction\n",
    "\t\t- **response_body_len** *int* The content size of the data transferred from the server's http service\n",
    "\t\t- **ct_srv_src** *int* No. of Connection that contain the same service (14) and source address in 100 connections according to the last time\n",
    "\t\t- **ct_state_ttl** *int* No. for each state (6) according to specific range if value for source/destination time to live\n",
    "\t\t- **ct_dst_ltm** *int* No of connectiuon of the same destination address (3) in 100 connections according to the last time\n",
    "\t\t- **ct_src_dport_ltm** *int* No. of connection of the same source address and the destination port in 100 connections according to the last time\n",
    "\t\t- **ct_dst_sport_ltm** *int* No. of connnection of the same destination address(3) and the source port (2) in 100 connection according to the last time (26).\n",
    "\t\t- **ct_dst_src_ltm** *int* No connection of the same cource (1) and the destination (3) address in 100 connection according to the last time (26)\n",
    "\t\t- **is_ftp_login** *byte* If the ftp session is accessed by user and password then 1 else 0\n",
    "\t\t- **ct_ftp_cmd** *int* No. of flows that has a command in ftp session\n",
    "\t\t- **ct_flw_http_mthd** *int* No. of flows that has methof such as Get and Post in http service\n",
    "\t\t- **ct_src_ltm** *int* No of connections of the same source address (1) in 100 connection according to the last time(26).\n",
    "\t\t- **ct_srv_dst** *int* No. of connectio that conbtain the same service and destination address in 100 connection according to the last time\n",
    "\t\t- **is_sm_ips_ports** *byte* If source equals to destination (3) IP addresses and port numbers (2)(4) are equal, this variable takes value 1 else 0\n",
    "\t\t- **attack_cat** *Nominal* The name of each attack category. In this data set, nine categories (e.g. Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms)\n",
    "\t\t- **label** *byte* 0 for normal and 1 for attack records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 82332 entries, 0 to 82331\n",
      "Data columns (total 45 columns):\n",
      "﻿id                  82332 non-null int64\n",
      "dur                  82332 non-null float64\n",
      "proto                82332 non-null object\n",
      "service              82332 non-null object\n",
      "state                82332 non-null object\n",
      "spkts                82332 non-null int64\n",
      "dpkts                82332 non-null int64\n",
      "sbytes               82332 non-null int64\n",
      "dbytes               82332 non-null int64\n",
      "rate                 82332 non-null float64\n",
      "sttl                 82332 non-null int64\n",
      "dttl                 82332 non-null int64\n",
      "sload                82332 non-null float64\n",
      "dload                82332 non-null float64\n",
      "sloss                82332 non-null int64\n",
      "dloss                82332 non-null int64\n",
      "sinpkt               82332 non-null float64\n",
      "dinpkt               82332 non-null float64\n",
      "sjit                 82332 non-null float64\n",
      "djit                 82332 non-null float64\n",
      "swin                 82332 non-null int64\n",
      "stcpb                82332 non-null int64\n",
      "dtcpb                82332 non-null int64\n",
      "dwin                 82332 non-null int64\n",
      "tcprtt               82332 non-null float64\n",
      "synack               82332 non-null float64\n",
      "ackdat               82332 non-null float64\n",
      "smean                82332 non-null int64\n",
      "dmean                82332 non-null int64\n",
      "trans_depth          82332 non-null int64\n",
      "response_body_len    82332 non-null int64\n",
      "ct_srv_src           82332 non-null int64\n",
      "ct_state_ttl         82332 non-null int64\n",
      "ct_dst_ltm           82332 non-null int64\n",
      "ct_src_dport_ltm     82332 non-null int64\n",
      "ct_dst_sport_ltm     82332 non-null int64\n",
      "ct_dst_src_ltm       82332 non-null int64\n",
      "is_ftp_login         82332 non-null int64\n",
      "ct_ftp_cmd           82332 non-null int64\n",
      "ct_flw_http_mthd     82332 non-null int64\n",
      "ct_src_ltm           82332 non-null int64\n",
      "ct_srv_dst           82332 non-null int64\n",
      "is_sm_ips_ports      82332 non-null int64\n",
      "attack_cat           82332 non-null object\n",
      "label                82332 non-null int64\n",
      "dtypes: float64(11), int64(30), object(4)\n",
      "memory usage: 28.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>﻿id</th>\n",
       "      <th>dur</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>dload</th>\n",
       "      <th>sloss</th>\n",
       "      <th>dloss</th>\n",
       "      <th>sinpkt</th>\n",
       "      <th>dinpkt</th>\n",
       "      <th>sjit</th>\n",
       "      <th>djit</th>\n",
       "      <th>swin</th>\n",
       "      <th>stcpb</th>\n",
       "      <th>dtcpb</th>\n",
       "      <th>dwin</th>\n",
       "      <th>tcprtt</th>\n",
       "      <th>synack</th>\n",
       "      <th>ackdat</th>\n",
       "      <th>smean</th>\n",
       "      <th>dmean</th>\n",
       "      <th>trans_depth</th>\n",
       "      <th>response_body_len</th>\n",
       "      <th>ct_srv_src</th>\n",
       "      <th>ct_state_ttl</th>\n",
       "      <th>ct_dst_ltm</th>\n",
       "      <th>ct_src_dport_ltm</th>\n",
       "      <th>ct_dst_sport_ltm</th>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_ftp_cmd</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "      <th>ct_srv_dst</th>\n",
       "      <th>is_sm_ips_ports</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41166.500000</td>\n",
       "      <td>1.006756</td>\n",
       "      <td>18.666472</td>\n",
       "      <td>17.545936</td>\n",
       "      <td>7.993908e+03</td>\n",
       "      <td>1.323379e+04</td>\n",
       "      <td>8.241089e+04</td>\n",
       "      <td>180.967667</td>\n",
       "      <td>95.713003</td>\n",
       "      <td>6.454902e+07</td>\n",
       "      <td>6.305470e+05</td>\n",
       "      <td>4.753692</td>\n",
       "      <td>6.308556</td>\n",
       "      <td>755.394301</td>\n",
       "      <td>121.701284</td>\n",
       "      <td>6.363075e+03</td>\n",
       "      <td>535.180430</td>\n",
       "      <td>133.45908</td>\n",
       "      <td>1.084642e+09</td>\n",
       "      <td>1.073465e+09</td>\n",
       "      <td>128.28662</td>\n",
       "      <td>0.055925</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>0.026669</td>\n",
       "      <td>139.528604</td>\n",
       "      <td>116.275069</td>\n",
       "      <td>0.094277</td>\n",
       "      <td>1.595372e+03</td>\n",
       "      <td>9.546604</td>\n",
       "      <td>1.369273</td>\n",
       "      <td>5.744923</td>\n",
       "      <td>4.928898</td>\n",
       "      <td>3.663011</td>\n",
       "      <td>7.456360</td>\n",
       "      <td>0.008284</td>\n",
       "      <td>0.008381</td>\n",
       "      <td>0.129743</td>\n",
       "      <td>6.468360</td>\n",
       "      <td>9.164262</td>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.550600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23767.345519</td>\n",
       "      <td>4.710444</td>\n",
       "      <td>133.916353</td>\n",
       "      <td>115.574086</td>\n",
       "      <td>1.716423e+05</td>\n",
       "      <td>1.514715e+05</td>\n",
       "      <td>1.486204e+05</td>\n",
       "      <td>101.513358</td>\n",
       "      <td>116.667722</td>\n",
       "      <td>1.798618e+08</td>\n",
       "      <td>2.393001e+06</td>\n",
       "      <td>64.649620</td>\n",
       "      <td>55.708021</td>\n",
       "      <td>6182.615732</td>\n",
       "      <td>1292.378499</td>\n",
       "      <td>5.672402e+04</td>\n",
       "      <td>3635.305383</td>\n",
       "      <td>127.35700</td>\n",
       "      <td>1.390860e+09</td>\n",
       "      <td>1.381996e+09</td>\n",
       "      <td>127.49137</td>\n",
       "      <td>0.116022</td>\n",
       "      <td>0.070854</td>\n",
       "      <td>0.055094</td>\n",
       "      <td>208.472063</td>\n",
       "      <td>244.600271</td>\n",
       "      <td>0.542922</td>\n",
       "      <td>3.806697e+04</td>\n",
       "      <td>11.090289</td>\n",
       "      <td>1.067188</td>\n",
       "      <td>8.418112</td>\n",
       "      <td>8.389545</td>\n",
       "      <td>5.915386</td>\n",
       "      <td>11.415191</td>\n",
       "      <td>0.091171</td>\n",
       "      <td>0.092485</td>\n",
       "      <td>0.638683</td>\n",
       "      <td>8.543927</td>\n",
       "      <td>11.121413</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>0.497436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20583.750000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.140000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.860611e+01</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.120247e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41166.500000</td>\n",
       "      <td>0.014138</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.340000e+02</td>\n",
       "      <td>1.780000e+02</td>\n",
       "      <td>2.650177e+03</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.770032e+05</td>\n",
       "      <td>2.112951e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.557928</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.762392e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.788886e+07</td>\n",
       "      <td>2.856975e+07</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61749.250000</td>\n",
       "      <td>0.719360</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.280000e+03</td>\n",
       "      <td>9.560000e+02</td>\n",
       "      <td>1.111111e+05</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>6.514286e+07</td>\n",
       "      <td>1.585808e+04</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>63.409444</td>\n",
       "      <td>63.136369</td>\n",
       "      <td>3.219332e+03</td>\n",
       "      <td>128.459914</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.171310e+09</td>\n",
       "      <td>2.144205e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.105541</td>\n",
       "      <td>0.052595</td>\n",
       "      <td>0.048816</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>59.999989</td>\n",
       "      <td>10646.000000</td>\n",
       "      <td>11018.000000</td>\n",
       "      <td>1.435577e+07</td>\n",
       "      <td>1.465753e+07</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>5.268000e+09</td>\n",
       "      <td>2.082111e+07</td>\n",
       "      <td>5319.000000</td>\n",
       "      <td>5507.000000</td>\n",
       "      <td>60009.992000</td>\n",
       "      <td>57739.240000</td>\n",
       "      <td>1.483831e+06</td>\n",
       "      <td>463199.240100</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>4.294950e+09</td>\n",
       "      <td>4.294881e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>3.821465</td>\n",
       "      <td>3.226788</td>\n",
       "      <td>2.928778</td>\n",
       "      <td>1504.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>5.242880e+06</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ﻿id           dur         spkts         dpkts        sbytes  \\\n",
       "count  82332.000000  82332.000000  82332.000000  82332.000000  8.233200e+04   \n",
       "mean   41166.500000      1.006756     18.666472     17.545936  7.993908e+03   \n",
       "std    23767.345519      4.710444    133.916353    115.574086  1.716423e+05   \n",
       "min        1.000000      0.000000      1.000000      0.000000  2.400000e+01   \n",
       "25%    20583.750000      0.000008      2.000000      0.000000  1.140000e+02   \n",
       "50%    41166.500000      0.014138      6.000000      2.000000  5.340000e+02   \n",
       "75%    61749.250000      0.719360     12.000000     10.000000  1.280000e+03   \n",
       "max    82332.000000     59.999989  10646.000000  11018.000000  1.435577e+07   \n",
       "\n",
       "             dbytes          rate          sttl          dttl         sload  \\\n",
       "count  8.233200e+04  8.233200e+04  82332.000000  82332.000000  8.233200e+04   \n",
       "mean   1.323379e+04  8.241089e+04    180.967667     95.713003  6.454902e+07   \n",
       "std    1.514715e+05  1.486204e+05    101.513358    116.667722  1.798618e+08   \n",
       "min    0.000000e+00  0.000000e+00      0.000000      0.000000  0.000000e+00   \n",
       "25%    0.000000e+00  2.860611e+01     62.000000      0.000000  1.120247e+04   \n",
       "50%    1.780000e+02  2.650177e+03    254.000000     29.000000  5.770032e+05   \n",
       "75%    9.560000e+02  1.111111e+05    254.000000    252.000000  6.514286e+07   \n",
       "max    1.465753e+07  1.000000e+06    255.000000    253.000000  5.268000e+09   \n",
       "\n",
       "              dload         sloss         dloss        sinpkt        dinpkt  \\\n",
       "count  8.233200e+04  82332.000000  82332.000000  82332.000000  82332.000000   \n",
       "mean   6.305470e+05      4.753692      6.308556    755.394301    121.701284   \n",
       "std    2.393001e+06     64.649620     55.708021   6182.615732   1292.378499   \n",
       "min    0.000000e+00      0.000000      0.000000      0.000000      0.000000   \n",
       "25%    0.000000e+00      0.000000      0.000000      0.008000      0.000000   \n",
       "50%    2.112951e+03      1.000000      0.000000      0.557928      0.010000   \n",
       "75%    1.585808e+04      3.000000      2.000000     63.409444     63.136369   \n",
       "max    2.082111e+07   5319.000000   5507.000000  60009.992000  57739.240000   \n",
       "\n",
       "               sjit           djit         swin         stcpb         dtcpb  \\\n",
       "count  8.233200e+04   82332.000000  82332.00000  8.233200e+04  8.233200e+04   \n",
       "mean   6.363075e+03     535.180430    133.45908  1.084642e+09  1.073465e+09   \n",
       "std    5.672402e+04    3635.305383    127.35700  1.390860e+09  1.381996e+09   \n",
       "min    0.000000e+00       0.000000      0.00000  0.000000e+00  0.000000e+00   \n",
       "25%    0.000000e+00       0.000000      0.00000  0.000000e+00  0.000000e+00   \n",
       "50%    1.762392e+01       0.000000    255.00000  2.788886e+07  2.856975e+07   \n",
       "75%    3.219332e+03     128.459914    255.00000  2.171310e+09  2.144205e+09   \n",
       "max    1.483831e+06  463199.240100    255.00000  4.294950e+09  4.294881e+09   \n",
       "\n",
       "              dwin        tcprtt        synack        ackdat         smean  \\\n",
       "count  82332.00000  82332.000000  82332.000000  82332.000000  82332.000000   \n",
       "mean     128.28662      0.055925      0.029256      0.026669    139.528604   \n",
       "std      127.49137      0.116022      0.070854      0.055094    208.472063   \n",
       "min        0.00000      0.000000      0.000000      0.000000     24.000000   \n",
       "25%        0.00000      0.000000      0.000000      0.000000     57.000000   \n",
       "50%      255.00000      0.000551      0.000441      0.000080     65.000000   \n",
       "75%      255.00000      0.105541      0.052595      0.048816    100.000000   \n",
       "max      255.00000      3.821465      3.226788      2.928778   1504.000000   \n",
       "\n",
       "              dmean   trans_depth  response_body_len    ct_srv_src  \\\n",
       "count  82332.000000  82332.000000       8.233200e+04  82332.000000   \n",
       "mean     116.275069      0.094277       1.595372e+03      9.546604   \n",
       "std      244.600271      0.542922       3.806697e+04     11.090289   \n",
       "min        0.000000      0.000000       0.000000e+00      1.000000   \n",
       "25%        0.000000      0.000000       0.000000e+00      2.000000   \n",
       "50%       44.000000      0.000000       0.000000e+00      5.000000   \n",
       "75%       87.000000      0.000000       0.000000e+00     11.000000   \n",
       "max     1500.000000    131.000000       5.242880e+06     63.000000   \n",
       "\n",
       "       ct_state_ttl    ct_dst_ltm  ct_src_dport_ltm  ct_dst_sport_ltm  \\\n",
       "count  82332.000000  82332.000000      82332.000000      82332.000000   \n",
       "mean       1.369273      5.744923          4.928898          3.663011   \n",
       "std        1.067188      8.418112          8.389545          5.915386   \n",
       "min        0.000000      1.000000          1.000000          1.000000   \n",
       "25%        1.000000      1.000000          1.000000          1.000000   \n",
       "50%        1.000000      2.000000          1.000000          1.000000   \n",
       "75%        2.000000      6.000000          4.000000          3.000000   \n",
       "max        6.000000     59.000000         59.000000         38.000000   \n",
       "\n",
       "       ct_dst_src_ltm  is_ftp_login    ct_ftp_cmd  ct_flw_http_mthd  \\\n",
       "count    82332.000000  82332.000000  82332.000000      82332.000000   \n",
       "mean         7.456360      0.008284      0.008381          0.129743   \n",
       "std         11.415191      0.091171      0.092485          0.638683   \n",
       "min          1.000000      0.000000      0.000000          0.000000   \n",
       "25%          1.000000      0.000000      0.000000          0.000000   \n",
       "50%          3.000000      0.000000      0.000000          0.000000   \n",
       "75%          6.000000      0.000000      0.000000          0.000000   \n",
       "max         63.000000      2.000000      2.000000         16.000000   \n",
       "\n",
       "         ct_src_ltm    ct_srv_dst  is_sm_ips_ports         label  \n",
       "count  82332.000000  82332.000000     82332.000000  82332.000000  \n",
       "mean       6.468360      9.164262         0.011126      0.550600  \n",
       "std        8.543927     11.121413         0.104891      0.497436  \n",
       "min        1.000000      1.000000         0.000000      0.000000  \n",
       "25%        1.000000      2.000000         0.000000      0.000000  \n",
       "50%        3.000000      5.000000         0.000000      1.000000  \n",
       "75%        7.000000     11.000000         0.000000      1.000000  \n",
       "max       60.000000     62.000000         1.000000      1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.describe() calculates the count, mean, standard deviation, min, max, and quartiles \n",
    "# of the data less the nominal (object) columns. \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "proto:\n",
      "['udp' 'arp' 'tcp' 'igmp' 'ospf' 'sctp' 'gre' 'ggp' 'ip' 'ipnip' 'st2'\n",
      " 'argus' 'chaos' 'egp' 'emcon' 'nvp' 'pup' 'xnet' 'mux' 'dcn' 'hmp' 'prm'\n",
      " 'trunk-1' 'trunk-2' 'xns-idp' 'leaf-1' 'leaf-2' 'irtp' 'rdp' 'netblt'\n",
      " 'mfe-nsp' 'merit-inp' '3pc' 'idpr' 'ddp' 'idpr-cmtp' 'tp++' 'ipv6' 'sdrp'\n",
      " 'ipv6-frag' 'ipv6-route' 'idrp' 'mhrp' 'i-nlsp' 'rvd' 'mobile' 'narp'\n",
      " 'skip' 'tlsp' 'ipv6-no' 'any' 'ipv6-opts' 'cftp' 'sat-expak' 'ippc'\n",
      " 'kryptolan' 'sat-mon' 'cpnx' 'wsn' 'pvp' 'br-sat-mon' 'sun-nd' 'wb-mon'\n",
      " 'vmtp' 'ttp' 'vines' 'nsfnet-igp' 'dgp' 'eigrp' 'tcf' 'sprite-rpc' 'larp'\n",
      " 'mtp' 'ax.25' 'ipip' 'aes-sp3-d' 'micp' 'encap' 'pri-enc' 'gmtp' 'ifmp'\n",
      " 'pnni' 'qnx' 'scps' 'cbt' 'bbn-rcc' 'igp' 'bna' 'swipe' 'visa' 'ipcv'\n",
      " 'cphb' 'iso-tp4' 'wb-expak' 'sep' 'secure-vmtp' 'xtp' 'il' 'rsvp' 'unas'\n",
      " 'fc' 'iso-ip' 'etherip' 'pim' 'aris' 'a/n' 'ipcomp' 'snp' 'compaq-peer'\n",
      " 'ipx-n-ip' 'pgm' 'vrrp' 'l2tp' 'zero' 'ddx' 'iatp' 'stp' 'srp' 'uti' 'sm'\n",
      " 'smp' 'isis' 'ptp' 'fire' 'crtp' 'crudp' 'sccopmce' 'iplt' 'pipe' 'sps'\n",
      " 'ib']\n",
      "service:\n",
      "['-' 'http' 'ftp' 'ftp-data' 'smtp' 'pop3' 'dns' 'snmp' 'ssl' 'dhcp' 'irc'\n",
      " 'radius' 'ssh']\n",
      "state:\n",
      "['INT' 'FIN' 'REQ' 'ACC' 'CON' 'RST' 'CLO']\n",
      "attack_cat:\n",
      "['Normal' 'Reconnaissance' 'Backdoor' 'DoS' 'Exploits' 'Analysis' 'Fuzzers'\n",
      " 'Worms' 'Shellcode' 'Generic']\n"
     ]
    }
   ],
   "source": [
    "# List the unique values of each nominal column\n",
    "header = df.columns.values.tolist()\n",
    "for column in header:\n",
    "    if column in df.select_dtypes(include=[\"object\"]):\n",
    "        print column + \":\\n\", df[column].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Ravi\n",
    "# Boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify data quality: Explain any missing values, duplicate data, and outliers.\n",
    "    - Are those mistakes? How do you deal with these problems? Be specific."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xad675f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAECCAYAAAAB2kexAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCvk/4xYGwdwCqbUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe4a6c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe53d290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAECCAYAAAAxVlaQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWqQ4QMX7OLcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb036750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vSSqQ8ZakAhlvSSqQ8ZakAv0vmV4ROirybNMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe3c1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gvUAfMAj8ELgfOA7sycz1Ve9a4CbgDWAwMx+NiAXAg8BiYBS4ITP3R8RlwOaqd2dm3l6N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\nHD0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103d8450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Daniel\n",
    "# is_ftp_login\n",
    "# mention ID feature\n",
    "\n",
    "# Ravi \n",
    "# pick out the box plots with outliers\n",
    "plt.boxplot(x=[df.sttl],labels=['sttl'])\n",
    "plt.show()\n",
    "\n",
    "plt.boxplot(x=[df.dttl],labels=['dttl'])\n",
    "plt.show()\n",
    "\n",
    "plt.boxplot(x=[df.trans_depth],labels=['trans_depth'])\n",
    "plt.show()\n",
    "\n",
    "plt.boxplot(x=[df.ct_srv_src],labels=['ct_srv_src'])\n",
    "plt.show()\n",
    "\n",
    "plt.boxplot(x=[df.ct_dst_src_ltm],labels=['ct_dst_src_ltm'])\n",
    "plt.show()\n",
    "\n",
    "plt.boxplot(x=[df.rate],labels=['rate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Give simple, appropriate statistics (range, mode, mean, median, variance, counts, etc.) for the most important attributes and describe what they mean or if you found something interesting. \n",
    "    - Note: You can also use data from other sources for comparison. Explain the significance of the statistics run and why they are meaningful. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Daniel \n",
    "# explain the five we chose"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the most important attributes appropriately (at least 5 attributes).\n",
    "    - Important: Provide an interpretation for each chart. Explain for each attribute why the chosen visualization is appropriate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd3ce830>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd421650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd3d99d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c31610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa32ca50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa320ff0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplots - Ravi each of the five by attack label\n",
    "# other visualizations as found\n",
    "#sttl\n",
    "df.boxplot(column=\"sttl\", by=\"label\")\n",
    "#dttl\n",
    "df.boxplot(column=\"dttl\", by=\"label\")\n",
    "plt.show()\n",
    "#trans_depth\n",
    "df.boxplot(column=\"trans_depth\", by=\"label\")\n",
    "#ct_srv_src\n",
    "df.boxplot(column=\"ct_srv_src\", by=\"label\")\n",
    "plt.show()\n",
    "#ct_dst_src_ltm\n",
    "df.boxplot(column=\"ct_dst_src_ltm\", by=\"label\")\n",
    "#rate\n",
    "df.boxplot(column=\"rate\", by=\"label\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#df_grouped = df.groupby(by=[,])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore relationships between attributes: Look at the attributes via scatter plots, correlation, cross-tabulation, group-wise averages, etc. as appropriate. \n",
    "    - Explain any interesting relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Heat plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify and explain interesting relationships between features and the class you are trying to predict (i.e., relationships with variables and the target classification)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Randy\n",
    "\n",
    "#positive correlation:\n",
    "#    sttl : 0.5\n",
    "#    ct_dst_sport_ltm : 0.39\n",
    "#    ct_src_dport_ltm : 0.34\n",
    "#negative correlation:\n",
    "#    swin : 0.41\n",
    "#    dwin : 0.37"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Randy - Do additional visualization to find something interesting\n",
    "When it is attack, the swin value is either 0 or 255, where as normal traffic has ranges between 0 and 255 inclusive.\n",
    "    - 30k attack records when swin/dwin is 0\n",
    "    - 15k attack records when swin/dwin is 255\n",
    "    \n",
    "Next break down by attack categories - most are the generic category *(cipher attack)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Are there other features that could be added to the data or created from existing features? Which ones? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# team "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAFxcXGA0GlFQUFDfm0xERGS26r1Fwd7eHpcvX8aAAQNw8+ZNrF69GsePH6/wvFarhU6n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\nREREJItBgYiIiGQxKBAREZGs/w+jBYJs/FQjoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b6c30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rate\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "ax = sns.boxplot(x=\"attack_cat\",y=\"rate\", hue= \"label\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zFRcX68EHH9Qf//hHbdu2TVOnTpUxRldeeaVmzpypnTt3avLkyfL29pbT6dTk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb00a3f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sttl\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "ax = sns.boxplot(x=\"attack_cat\",y=\"sttl\", hue= \"label\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rys/P1zfffKPIyEj5+fkpNDRUYWFhysrK+u1rdhlkZGSoXbt2kk6dYOa77767zBUBAK4E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\nANgcYQ4AgM0R5gAA2Nz/B6M3y+A84hq0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xce69c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dttl\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "ax = sns.boxplot(x=\"attack_cat\",y=\"dttl\", hue= \"label\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1nb+tSE9PV69evVSvXr1L1s277rpL9erVc76f504sq1WrlmrUqKH5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\n+39WeGRWYlHKhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe074910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#trans_depth\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "ax = sns.boxplot(x=\"attack_cat\",y=\"trans_depth\", hue= \"label\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TJk0AABEREZg8ebLhKiQiIiKJzvNxl4Y2ACm0AeDo0aP6rYiIiIgqpXNwV4YjqhGRuXNy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf84dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ct_srv_src\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "ax = sns.boxplot(x=\"attack_cat\",y=\"ct_srv_src\", hue= \"label\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HxcUFWq0WWq0WLi4u5g5HtXjcIVNSXeJmByxE98THx0Ov10Ov1yM+Pt7c4agW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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf0d450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ct_dst_src_ltm\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "ax = sns.boxplot(x=\"attack_cat\",y=\"ct_dst_src_ltm\", hue= \"label\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HR8PFxQU//PADCgoKEB0djYULF9Z1bURERGRltQr+kpIS9O7dGz/++CP+53/+B0FBQSgr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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb00d910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#dpkts\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "ax = sns.boxplot(x=\"attack_cat\",y=\"dpkts\", hue= \"label\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
